A ‘lite’ version of the EQ-5D-5L valuation protocol, which requires a smaller sample by collecting more data from each participant, was proposed and used to develop an EQ-5D-5L value set for Uganda.Adult respondents from the general Ugandan population were quota sampled based on age and sex. Eligible participants were asked to complete 20 composite time trade-off tasks in the tablet-assisted personal interviews using the offline EuroQol Portable Valuation Technology software under routine quality control. No discrete choice experiment task was administered.The composite time trade-off data were modelled using four additive and two multiplicative regression models. Model performance was evaluated based on face validity, prediction accuracy in cross-validation and in predicting mild health states. The final value set was generated using the best-performing model.N = 545) participated in this study. Responses to composite time trade-off tasks from 492 participants were included in the primary analysis. All models showed face validity and generated comparable prediction accuracy. The Tobit model with constrained intercepts and corrected for heteroscedasticity was considered the preferred model for the value set on the basis of better performance. The value set ranges from − 1.116 (state 55555) to 1 (state 11111) with ‘pain/discomfort’ as the most important dimension.A representative sample (This is the first EQ-5D-5L valuation study using a ‘lite’ protocol involving composite time trade-off data only. Our results suggest its feasibility in resource-constrained settings. The established EQ-5D-5L value set for Uganda is expected to be used for economic evaluations and decision making in Uganda and the East Africa region.The online version contains supplementary material available at 10.1007/s40273-021-01101-x. The EQ-5D family of instruments has been widely used around the world as a measure of health outcomes to inform resource allocation and decision making. The EQ-5D covers five dimensions of health . The newer version (EQ-5D-5L) has five response levels for each dimension . Empirical evidence has demonstrated the superiority of EQ-5D-5L over the earlier version (EQ-5D-3L) in terms of measurement properties, such as reduced ceiling effects and greater discrimination among known groups –4.The value set accompanying EQ-5D represents the preferences of the general population of a country/region for health states defined by EQ-5D. It generates preference-based health-related quality-of-life scores on a scale anchored at 1  and 0 (dead), which allows quality-adjusted life-year (QALY) calculations, often used in economic evaluations. The EuroQol group developed a standardised valuation study protocol, the EuroQol Valuation Technology (EQ-VT), to create value sets for the EQ-5D-5L –11, EuroIn African countries, methods of health technology assessment (HTA) including economic evaluations have been increasingly used to inform the allocation of scarce healthcare resources , 24. HowThe EQ-VT protocol includes composite time trade-off (cTTO) and discrete choice experiment (DCE) tasks . The cTTTaking all information into consideration, we proposed a ‘lite’ valuation protocol, which requires a reduced sample size by collecting more cTTO data from each respondent. The valuation study was undertaken in Uganda, one of the major focuses of clinical trial research for low-to-middle income countries, specialising particularly, though not only, in human immunodeficiency virus research –32. The The EuroQol Portable Valuation Technology (EQ-PVT) software, a portable version of EQ-VT, which collects data using an offline tool in macro-enabled Microsoft PowerPoint, was adapted to accommodate the design of this ‘lite’ version.Considering the good psychometric properties of time trade-off (TTO) in evaluating health states in Ugandan individuals , cTTO wa5) health states defined by EQ-5D-5L dimensions and response levels to represent a wide range of health problems, divided into ten blocks of ten health states. All ten blocks included the worst state (55555), one state with mild problems in one dimension only, and eight states unique to each block that varied in severity. The standard valuation approach is 1000 respondents each valuing ten health states.The EQ-VT  design sN = 500). This resulted in the same number of cTTO responses as the standard valuation approach (500 respondents × 20 tasks = 1000 respondents × 10 tasks). Each of the ten blocks of ten health states was combined with its adjacent block to form ten new blocks, i.e. 1 and 2, 2 and 3, 3 and 4, 4 and 5, 5 and 6, 6 and 7, 7 and 8, 8 and 9, 9 and 10, 10 and 1. The repeated health state 55555 in each block was replaced with one of the five severe health sates . These newly formed ten blocks of health states included 91 health states, with 20 health states per block. Each participant was randomly assigned to one of the ten blocks.We adapted the EQ-VT design to allow 20 health states to be valued by each respondent from a smaller sample , covering approximately 30% of the Ugandan general population . Data weTo enable interviewers to focus on the valuation tasks, we recruited two research assistants as field mobilisers to help with participant recruitment. The field mobilisation, including area selection, local council communication and participant identification, was conducted prior to the data collection in each district. Adults who were able to understand the cTTO tasks (as judged by the mobilisers and interviewers) were eligible to participate. Each respondent received Ugandan Shilling 25,000 (US$7) to compensate for the time and travel expenses.Informed written consent was sought and granted before the interview. All surveys were completed through interviewer-assisted data collection using Windows-based tablets with the EQ-PVT in Luganda. First, participants were asked to provide information about demographic characteristics . Second, they reported their current level of health as described by the EQ-5D-5L . Third, participants completed the cTTO valuation tasks, as instructed by the interviewers, including the wheelchair example , three practice health states  and 20 health states. Last, they answered questions about their socioeconomic status , health conditions (e.g. illness) and understanding of health .Following the EQ-VT guideline that each interviewer is expected to complete 80–100 interviews, we recruited six interviewers. They, together with the two field mobilisers, received face-to-face, a full-week training before the fieldwork, delivered by the lead health economists who were trained by EuroQol using EQ-VT . The traThe quality of cTTO data was checked based on several criteria . These cOne previous study comparing different modelling methods for EQ-5D-5L value sets concluded that a model with parameters for all dimensions and levels performed best . Some isε the error term and μ the respondent-level random intercept. Model 2 is an additive 20-parameter Tobit model, accommodating the left-censored nature of cTTO data. The Tobit model assumes a latent variable (cTTO*) underlying the observed cTTO values. The latent variable (cTTO*) can take on values beyond the range of the observed values censored at − 1. The Tobit model uses a likelihood function to adjust the parameter estimates for the probability of the latent preferences being beyond the censored value, that is, if cTTO* ≤ − 1, observed cTTO = − 1 and if cTTO* > − 1, observed cTTO = cTTO*:Model 1 is an additive 20-parameter linear regression model. The 20 parameters (4 levels × 5 dimensions) represent the value decrement assigned to the level-dimension combinations of EQ-5D-5L health states, with level 1 (no problems) as the baseline  and three parameters for levels 2, 3 and 4 problems   is used to distinguish level 5 in pain/discomfort and anxiety/depression dimensions (‘extreme’) to level 5 in mobility, self‐care and usual activity dimensions (‘unable’)  Eq. . Thus, tWe set some criteria to select the preferred model. The first criterion was the face validity, for which the model should generate logically consistent parameter estimates (i.e. a larger decrement with more severe problems). The second criterion was the prediction accuracy of models in predicting values for health states. A leave-out-by-state cross-validation method was used by excluding each health state in turn from estimating model coefficients and then calculating the predicted values for the left-out state using the fitted model. Similarly, a leave-out-by-block cross-validation was conducted by excluding one block of health states to estimate model coefficients and to predict values for the states in the left-out block. We also examined the prediction accuracy for 11 mild health states (level 1/2 in maximum 2 dimensions), such as state 11122. Two types of prediction errors, mean absolute error (MAE) and root mean squared error (RMSE), were calculated using the predicted and observed mean values for health states, with lower MAE/RMSE being favoured. In the event of inconsistent results in the comparison, we looked at the absolute values in the MAE/RMSE to assist in selecting the preferred model.Re-inclusion of data from respondents who had severe inconsistency in responses (i.e. full sample),Exclusion of data from each interviewer in turn to examine interviewer effects.The best-performing model was used to develop the value set. The following sensitivity analyses were conducted to evaluate the robustness of the primary analysis:intreg was used for models 3 and 4 and the command menl for models 5 and 6.In the analysis, we used rescaled cTTO values , which results in the values on a scale between 0 and 2, the intercept suppressed to 0, and parameter estimates being positive, for easier comparison between models. All analyses were conducted using Stata 16.1 (StataCorp LLC). The command In total, 545 participants were recruited with complete data. Responses from 53 (7.7%) participants were flagged as having severe inconsistency, and their responses were excluded from the primary analysis, resulting in a sample of 492 participants. The full sample and analytic sample were generally representative of the Ugandan adult population in terms of age and sex Table , althougRespondents in the analytic sample took an average of 8.9 ± 5.3 iterative steps before reaching the point of indifference. Mean time spent on 20 tasks was 30.1 ± 10.9 minutes. The main analysis included 9840 cTTO responses from 492 participants (492 × 20 = 9840), of which 4361 (44.3%) were negative . For example, for the health state 23514, the value would be 1 − (0.073 + 0.110 + 0.306 + 0 + 0.235) = 0.276. The predicted EQ-5D-5L values ranged from − 1.116 (for state 55555) to 1. Figure Using the full sample, all models demonstrated face validity (Table S3 of the ESM); model 2 with unconstrained intercepts generated the smallest MAE and RMSE in cross-validation analyses and model 4 with constrained intercepts performed the best in predicting mild states (Table S4 of the ESM). After looking at the absolute values in MAE/RMSE, model 4 with constrained intercepts was the preferred model, consistent with the main analysis. The model parameter estimates using the analytic sample and the full sample were almost identical .In this study, for the first time, we used a ‘lite’ protocol that collected more cTTO data from half of the sample size recommended in the standard protocol. Our results provide evidence of the successful completion of this protocol, combined with intensive interviewer training and data monitoring. The value set generated would be the first EQ-5D-5L value set in Uganda, the second in East Africa (following Ethiopia ) and theThe design of this ‘lite’ protocol that collected more cTTO data from fewer participants was informed by findings from existing studies. A substantial number of published EQ-5D-5L value sets are based on cTTO data only, such as Canada , China , the NetBased on the pre-specified criteria, the Tobit model with constrained intercepts, corrected for heteroskedasticity, was the preferred model for generating the value set. In cross-validation analyses, all models showed similar MAE/RMSE while the multiplicative model 5 had the lowest MAE/RMSE in leave-out-by-state cross-validation. The multiplicative models make a very strong assumption on the structure of the EQ-5D-5L dimensions and levels, which was not reflected in other models that were able to identify coefficients for all 20 parameters independently. We also considered the intercept of the models, which indicates the highest value that the model could predict (for state 11111). A previous study highlighted that allowing a large gap between the predicted value for state 11111 and 1  could result in over-investment in treatments for very mild health problems as the intended use of value sets is used to inform priority decisions ; thus, w“which dimension is most/least important relating to your health?” (Table S6 of the ESM), supporting the validity of the value set. Compared to other countries, pain/discomfort was considered the most important in the USA [okweraliikirira/okwenyamira) is not a native concept to the Ugandan cultures where mental health is less focused. It is generally taboo  and considered a weakness for someone to show signs of mental illness or even the need for mental healthcare. Thus, it is unsurprising that the mental health is rated least, consistent with findings observed in previous research [The final EQ-5D-5L value set for Uganda suggests that pain/discomfort is the most important dimension while anxiety/depression the least. These results were in line with the responses from participants who were directly asked  the USA  and Germ the USA  and the  the USA . Interes the USA . This miresearch .The value set generates the maximum predicted value  at 0.950 for state 11112 and the minimum value at − 1.116 for state 55555. Compared to the Ethiopia value set  with a rThe study’s limitations should be noted. Mainly, there may be issues with the sample’s representativeness. The higher education level in our sample was most likely because of the fact that people of lower education may have difficulties understanding the cTTO tasks. The ethnicity distribution may result from the data having been collected in the Central region where more people of the Baganda ethnic group live . In addiThis is the first EQ-5D-5L valuation study using a ‘lite’ protocol involving cTTO data only. Our results support its feasibility, which could benefit future valuation studies, especially in resource-constrained settings.This study established the EQ-5D-5L value set for Uganda. We expect it to serve as the foundation for sound health economic evaluations and HTA to inform decision making in the healthcare system in Uganda and the East Africa region.Supplementary file1 (DOCX 78 kb)Below is the link to the electronic supplementary material.


This study aimed to increase the knowledge on how to enhance the performance of artificial intelligence (AI)-enabled electrocardiography (ECG) to detect atrial fibrillation (AF) on sinus rhythm ECG (SR-ECG).It is a retrospective analysis of a single-center, prospective cohort study (Shinken Database). We developed AI-enabled ECG using SR-ECG to predict AF with a convolutional neural network (CNN). Among new patients in our hospital , 276 AF label (having ECG on AF [AF-ECG] in the ECG database) and 1896 SR label with following three conditions were identified in the derivation dataset: (1) without structural heart disease, (2) in AF label, SR-ECG was taken within 31 days from AF-ECG, and (3) in SR label, follow-up ≥ 1,095 days. Three patterns of AF label were analyzed by timing of SR-ECG to AF-ECG . The outcome measurement was area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. As an extra-testing dataset, the performance of AI-enabled ECG was tested in patients with structural heart disease.The AUC of AI-enabled ECG with CNN algorithm 1, 2, and 3 in the derivation dataset was 0.83, 0.88, and 0.86, respectively; when tested in patients with structural heart disease, 0.75, 0.81, and 0.78, respectively.We confirmed high performance of AI-enabled ECG to detect AF on SR-ECG in patients without structural heart disease. The performance enhanced especially when SR-ECG after index AF-ECG was included in the algorithm, which was consistent in patients with structural heart disease. One major challenge is promptly diagnosing AF after onset because of its silent nature in many patients. As tools for screening AF, many devices have been proposed over the gold standard tool of 12-lead electrocardiography (ECG), such as patient-initiated devices , semi-continuous (smart watch ECG) or continuous wearable devices , and implanted devices In practical viewpoint, simple methods to discriminate patients at a high risk of AF would help to identify candidates for such long-term monitoring device. For example, precise analysis of waveforms of resting 12-lead ECG using artificial intelligence (AI) has identified patients with AF on sinus rhythm ECG (SR-ECG) After the publishment of these studies, additional important tasks in the methodologies have raised, one being labeling problems. For example, among ECG recordings with the AF label, ECG recordings in SR had different predictive abilities according to the length of time to incident AF In the present study, we developed AI-enabled ECG within SR-ECG to predict AF using a single-center ECG database to enhance the performance of AI-enabled ECG in special reference to these unresolved issues.22.1This study was performed in accordance with the Declaration of Helsinki (revised in 2013) and Ethical Guidelines for Medical and Health Research Involving Human Subjects . Written informed consent was obtained from all participants. The study protocol was reviewed by the Institutional Review Board of the Cardiovascular Institute.2.22.2.1The Shinken database includes all patients who newly visited the Cardiovascular Institute, Tokyo, Japan, excluding foreign travelers and patients with active cancer. This single-hospital database was established in June 2004. Details of this database have been described elsewhere 2.2.2Out of 17,195 patients in the total study population, 2172 patients were selected as the derivation dataset which comprised 276 patients with AF label and 1896 patients with SR label . In the As shown in the flowchart , patient2.2.3Among the patients who were excluded in the process of selecting patients for derivation dataset, followings were defined as extra testing datasets and used for testing the performance of AI-enabled ECG : (1) patThe patients in the extra testing dataset 2 were further divided according to the timing of the index SR-ECG from the first AF-ECG : ≤−366 d2.3Twelve-lead ECG was recorded for 10 s in the supine position using an ECG machine  at a sampling rate of 500 Hz, and raw data of digital records were stored using the MUSE data management system. Out of each 10-second ECG recording, 5-second ECG samples were extracted. The reason for sampling with a 5-second duration was because we employed oversampling to balance the number of samples between AF and SR labels 2.3.1In patients with the SR label, the index SR-ECG for the analysis was the one obtained at the initial visit. In the SR label, each 10-second ECG was divided at the half which yielded two samples of 5-second ECGs.In patients with the AF label, the index SR-ECG was the one nearest (within 31 days) the first AF-ECG in the ECG database. Here, the index SR-ECG with the AF label was chosen according to three patterns: the pre- , post- , or pre- or post-  31-day period of the first AF-ECG . As threIn each derivation dataset, ECG samples were divided into the training, validation, and testing datasets at a ratio of 7:1:2  backend and Python. Of the eight physical leads and four augmented leads with a 10-second duration on 12-lead electrocardiography (ECG) recordings, we selected the eight independent leads  with a 5-second duration. Accordingly, the original 12 × 5000 matrix  was reduced to an 8 × 2500 matrix.The CNN model had layers for a temporal axis and a lead axis The model was trained on a computer with 192-GB RAM and single Quadro P-2200 (NVIDIA) graphics processing units that were used to train the model using Keras.A receiver operating characteristic (ROC) curve was created to validate and test the data to assess the AUC of AI-enabled ECG to determine whether AF was present. Using the ROC curve in the validation dataset, we tuned the number of repetitions for the combination of the two residual blocks and average pooling written above (N). Moreover, we determined the probability threshold of AF using the ROC curve. These parameters were used for the final evaluation using the testing dataset.2.4.2The primary outcome of the study was the ability of AI-enabled ECG to identify patients with AF using SR-ECG, which was assessed by the AUC, sensitivity, specificity, accuracy, and F1 score of the model. In the derivation dataset and the extra testing dataset 1, the AUC, sensitivity, specificity, accuracy, and F1 score of the model were assessed. In the extra testing dataset 2 and 3, only the accuracy was tested because the datasets were consisted of single label .We used two-sided 95% confidence intervals (CIs) to summarize sample variability in the estimates. We used exact CIs (Clopper–Pearson) to be conservative for accuracy, sensitivity, and specificity. The CIs for the AUC were estimated using Sun’s and Su’s optimization of the Delong method using the pROC package 33.1Among all included patients in the derivation dataset , the mean age was 60.1 ± 13.6 years at the initial visit, and 1170 patients (53.9%) were male.The performance of AI-enabled ECG for CNN algorithm 1, 2, and 3 are shown in 3.2The results of testing the performance of AI-enabled ECG for CNN algorithm 1, 2, and 3 in the extra testing dataset 1 are shown in The results of testing the performance of AI-enabled ECG for CNN algorithm 1, 2, and 3 in the extra testing dataset 2 are shown in The results of testing the performance of AI-enabled ECG for CNN algorithm 1, 2, and 3 in the extra testing dataset 3 are shown in 44.1In the present study, we developed AI-enabled ECG to predict AF using 12-lead SR-ECG in patients without structural heart disease by three patterns according to the timing of the index SR-ECG, and thereafter confirmed the performance in patients with structural heart disease. The AUC of AI-enabled ECG was higher when the algorithm included SR-ECG taken after the AF-ECG . Similar tendency was observed when the AI-enabled ECG was tested in patients with structural heart disease .4.2AI-enabled ECG to predict AF using 12-lead SR-ECG has already been reported by multiple study groups Such studies were based on the hypothesis that the AF signature due to structural changes in the atria can be identified by 12-lead ECG during SR 4.3Attia et al. 2 and 37.56 mL/m2) First, the timing of SR-ECG to AF-ECG in AF-label would be important. In the present study, we developed three patterns of AI-enabled ECG deriving from three patterns of AF-label, where the index SR-ECG was taken before, after, or before-or-after the first AF-ECG . When we compared their performance, the AUC was higher when the algorithm included SR-ECG taken after the AF-ECG  than when it did not . Based on the result, we can learn two points: (1) AI-enabled ECG can detect the structural changes in atrium before AF incidence because the AUC was over 0.8 even when the model included only the information before the first AF-ECG , and (2) AI-enabled ECG enhanced the performance remarkably when the information after the first AF-ECG was included . These results are supported by previous clinical reports. In a study that investigated the changes in atrium between before and after direct cardioversion for AF, left atrial dimension did not decrease at 1 week after the cardioversion  Second, the tendency in the performance of three patterns of CNN algorithm in patients without structural heart disease was similar when they were applied to those with structural heart disease. Because patients with structural heart disease have various patterns of typical ECG features, especially in ST-T segment, we assumed that the existence of structural heart disease should increase the branches of patterns that AI-enabled ECG should learn and would affect its performance. Therefore, in the present study, by excluding patients with structural heart disease from our model, we intended to limit the variations in the derivation dataset. When we applied our model to patients with structural heart disease, the AUC was 0.75, 0.81, and 0.78 for CNN algorithm 1, 2, and 3, which were lower than the AUCs in patients without structural heart disease, but the tendency in the performance of three patterns of CNN algorithm was similar. Although the AUCs of AI-enabled ECG developed in patients without structural heart disease were lower in those with structural heart disease, they remained around 0.8, suggesting the AI-enabled ECG works to some extent beyond the absence or existence of structural heart disease. This may be because, even in patients with structural heart disease, typical ECG characteristics present only in limited patients with relatively severe conditions. These results possibly suggest that including patients with structural heart disease may not be a matter for developing AI-enabled ECG to detect AF on SR-ECG. Therefore, our model could be extrapolated to those with structural heart disease to some extent, but its utility requires further investigation.The AI-enabled ECG to detect AF on SR-ECG may be a candidate to be incorporated in the technology tools supporting the guidelines-recommended integrated management of AF 4.4There are several limitations of the present study that should be highlighted. First, although we limited SR-ECG recordings with the SR label to patients followed up for ≥ 1,095 days, there remained a possibility that undetected AF existed in patients with the SR label. Second, our study excluded patients with structural heart disease and can thus only be applied to similar populations. Third, our model of AI-enabled ECG should be verified against external datasets to confirm the generalizability.4.5We confirmed high performance of AI-enabled ECG to detect AF on SR-ECG in patients without structural heart disease. The performance enhanced especially when SR-ECG after index AF-ECG was included in the algorithm, which was consistent in patients with structural heart disease.10.13039/501100000008Ministry of Health, Labour and Welfare, Japan, issued in 2017). Written informed consent was obtained from all participants. The study protocol was reviewed by the Institutional Review Board of the Cardiovascular Institute.This study was performed in accordance with the ethical norms based on the Declaration of Helsinki (revised in 2013) and Ethical Guidelines for Medical and Health Research Involving Human Subjects .This study was partially supported by the Practical Research Project for Life-Style related Diseases including Cardiovascular Diseases and Diabetes Mellitus from the Japan Agency for Medical Research and Development, AMED (JP17ek0210082).Shinya Suzuki: Conceptualization, Validation, Writing – original draft. Jun Motogi: Conceptualization, Validation. Hiroshi Nakai: Conceptualization, Data curation, Validation. Wataru Matsuzawa: Data curation, Formal analysis. Tsuneo Takayanagi: Data curation, Formal analysis. Takuya Umemoto: Data curation, Formal analysis, Writing – original draft, Writing – review & editing. Naomi Hirota: Validation, Writing – review & editing. Akira Hyodo: Formal analysis, Validation. Keiichi Satoh: Formal analysis, Validation. Takayuki Otsuka: Writing – review & editing. Takuto Arita: Writing – review & editing. Naoharu Yagi: Writing – review & editing. Takeshi Yamashita: Funding acquisition, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper: .


Longissimus thoracis et lumborum) and their relationship with consumer flavour liking. Volatiles were measured in grilled steaks subjected to 35 days of dry ageing, 35 days of wet ageing, 56 days of dry ageing or 56 days of wet ageing, using headspace-solid-phase microextraction followed by gas chromatography-mass spectrometry. Gas chromatography-olfactometry-mass spectrometry was also conducted on 35-day wet and dry aged samples to identify volatiles with high odour impact. The concentration of many odour impact volatiles, e.g., 3-hydroxy-2-butanone, 2-acetyl-2-thiazoline, and various alkyl-pyrazines, was significantly higher in dry aged beef compared to wet aged beef (p < 0.05). Several odour impact volatiles, e.g., 2-acetyl-1-pyrroline, and alkyl-pyrazines, decreased significantly with ageing time (p < 0.05), while volatile products of lipid oxidation and microbial metabolism increased with ageing time. Partial least-squares regression analysis showed that the higher consumer flavour liking for 35-day dry aged beef was associated with higher concentrations of desirable odour-active volatiles.This study investigated the effect of ageing method and ageing time on the volatile profiles of grilled beef striploins ( Ageing in vacuum packs is widely used in the beef industry to preserve and to improve the eating quality of meat . Ageing Due to the consensus among consumers and retailers that dry ageing significantly improves the flavour of beef compared to that of wet ageing, the flavour chemistry of dry aged beef has been a subject of investigation in several studies ,6. Iida,The primary goal of this study was to analyse the effects of dry and wet ageing, and ageing time, on volatile profiles of beef. The relationship between volatiles and flavour liking of aged beef was also investigated to explain the difference in flavour liking of beef aged with different ageing methods.n = 24) from 16 steers and eight heifers with normal pH (<5.7) were selected at 24 h post mortem from a commercial beef processing plant in Tasmania, Australia. Full length bone-in longissimus thoracis et lumborum was excised from both sides of 24 carcasses. All primals were vacuum packed and transported in a refrigerated lorry to Top Cut Foods  for further processing and ageing.The sample collection process for this project was described fully in the study of Ha, et al. . The catlongissimus thoracis et lumborum primals from one carcass. There were seven combinations of treatments, and only the 35-day wet and dry aged, and the 56-day wet and dry aged samples from 12 randomly selected carcasses were used in the present study. Wet ageing was conducted with boneless primals in Cryovac® polyamide, polyethylene vacuum pack bags with an oxygen transmission rate of 20 cc/m2/24 h at 23 °C without illumination. The primals were placed in a refrigerated room with temperature fluctuating between 2–6 °C.The wet and dry ageing conditions were described in the study of Ha, et al. . Upon arThe dry ageing room was a multi-batch chiller with movable racks and two UV lights fitted to its ceiling. The meat samples were rotated daily to different positions in the chiller. The relative humidity (RH) in the chamber ranged from 53% to 100% with an average RH of 89.4% over the experimental period. The recorded temperature was 1.3 °C to 4.2 °C with an average temperature of 2.1 °C. The airspeed at the central position varied between 0.75 and 1.2 m/s.After each ageing period, steaks 2.5 cm thick, and small samples, were obtained from the primals and frozen at −20 °C for 3 months until sensory analysis or at −80 °C for four months until flavour chemistry analysis. Consumer flavour liking was obtained using Meat Standards Australia (MSA) untrained consumer panels with 900 participants as described by Ha, et al. [w/w).Intramuscular fat (IMF) was measured using near-infrared spectrometry as described by Perry, et al. . Frozen m/z 40–250. An aliphatic hydrocarbon mix  was used to determine linear retention indices. Compounds were identified by comparing their electron impact mass spectra with reference spectra in the National Institute of Standards and Technology mass spectral database (NIST 2002) and by linear retention indices matching those of published values (NIST Chemistry WebBook). Integrated area data were normalised to the internal standard and semi-quantitative data (µg/g) were estimated.Beef steaks were thawed overnight at 4 °C before testing and grilled for 3 min using a clamshell grill  set at 220 °C. The samples were then allowed to rest for another 3 min under aluminium foil. The grilled beef steaks were then weighed, roughly cut and Milli-Q water was added at a ratio of 1:2 (Milli-Q water:meat). Samples were macerated using a hand-held food processor and 4 g of slurry was transferred to a headspace vial. The internal standard 4-methyl-1-pentanol was placed into a 200 µL insert. Duplicate samples were placed in the autosampler . The samples were pre-incubated at 40 °C for 15 min and the headspace volatiles were extracted with divinylbenzene/carboxen/PDMS 23-gauge, 2 cm solid phase microextraction (SPME) fibres  for 40 min at 40 °C with agitation. The extracted volatiles were desorbed in splitless mode into a hot injector (250 °C) for 5 min and separated using gas chromatography–mass spectrometry  on a Zebron- Wax column  with the following temperature programming; initial temperature of 35 °C was held for 5 min and then heated at 5 °C/min to 250 °C. The electron impact (EI) mass spectrometer was programmed to scan the mass range n = 8 carcasses). A panel of trained GC-O assessors (n = 6) evaluated the column effluent of each matching pair (from the same carcass) of wet and dry aged samples using a validated direct intensity technique. Assessors were trained according to previously reported protocols [GC-O-MS analysis was conducted on a subset of 35-day dry aged and wet aged samples . The multivariate analysis of variance (MANOVA) was performed on the volatile data using the following model; Response (volatile data) = ageing method (main effect) + ageing time (main effect) + ageing method ∗ ageing time (interaction) + animal (block) + intramuscular fat content (covariate). The average standard error of difference (SED) were calculated for main effects and interactions. Effects of treatments were considered significant if the difference between treatments were greater than 2 × SED.Volatile data were analysed using GenStatn =12 carcasses in each ageing method ∗ ageing time treatment group, there was one carcass with missing sensory data in 56-day dry ageing and one in 56-day wet ageing groups respectively). Principal component analysis (PCA) was performed on flavour liking data of samples and their corresponding semi-quantitative volatile data with confirmed odour impact in GC-O analysis or literatures using Matlab . A measure of sample adequacy was also performed using the Kaiser–Meyer–Olkin (KMO) test to check the suitability of the selected variables for PCA. A partial least-squares regression analysis was performed on the mean-integrated volatile and sensory data for each treatment group using the partial least-squares (PLS) procedure in Genstat following the method described by Frank, et al. [n = 22) known to be odour-active in the GC-O results or the literature were selected for the final optimised PLS model. One latent factor was used in modelling with cross-validation procedures . The Osten’s F-test was performed on the predicted sum of square statistic to evaluate the significance of latent factor. The root mean square error of cross validation was calculated from the predicted residual sum of square statistic.The consumer flavour liking scores reported in Ha, et al.  were mat, et al. . All datA total of 62 volatiles were identified in the grilled beef headspace extracts based on EI mass spectra and retention indices . 2-Methyp < 0.05 except for heptanal) but decreased with the ageing in wet ageing . In the beef aged 56 days, the concentrations of these volatiles were higher in the dry aged meat compared to the wet aged meat (p < 0.05). The increase of these volatiles in dry ageing agrees with the results of thiobarbituric acid reactive substances (TBARS) obtained in the same samples [Aldehydes generally play important roles in the flavour of beef and quantitatively dominate many other odour-active volatiles in cooked beef . In meat samples . However samples , which i samples . Whereasp < 0.05, except for 2,5-dimethylpyrazine and 2,6-dimethylpyrazine), and the concentration tended to decrease with ageing time in both ageing methods . This result indicates a higher rate of Maillard reaction in dry aged beef compared to wet aged. The difference in Maillard reaction could be attributed to the ultimate pH as reported by Ha, et al. [p < 0.001), and the pH declined in both ageing methods with the ageing time. The study of Madruga and Mottram [p < 0.05). In addition to the Maillard reaction, the alkyl-pyrazines are known to be formed in the lipid-Maillard interaction [Significant effects of ageing method and ageing time were measured for alkyl-pyrazine compounds in the present study . Specifi, et al. . The dry Mottram  shows th Mottram  showed teraction ,29. The eraction ,9. Thereeraction ,35. AsidE)-2-nonenal), roasted (2-acetyl-2-thiazoline), and fatty (p-cresol) odours. The main grilled beef aroma peaks correspond with the Maillard-derived alkyl-pyrazines, which are well-known components of beef aroma [The average aromagram for the wet and dry aged grilled beef samples at 35-days ageing are shown in ef aroma . The aroef aroma . The higp < 0.05), whereas a small group of lipid oxidation products  and ethanol increased (p < 0.05). The study of Watanabe, et al. [M. biceps femoris for 2–30 days, indicating that the effect of ageing time on cooked beef volatiles could differ between cuts. A study on dry aged Hanwoo longissimus thoracis et lumborum showed a significant increase of volatile concentrations from day 0 to day 40, however, the change of volatile concentration after day 40 was not reported [Multivariate analysis was conducted on the GC-MS volatiles and consumer flavour liking data previously reported in Ha, et al.  to inves, et al.  showed treported . In the reported  reportedreported , the conn = 4, mean-centred values for treatment combinations) and does not take into consideration the important role of non-volatile flavour compounds such as free amino acids, peptides and nucleotides. In addition, the roles of particular volatiles in flavour liking needs to be further confirmed in omission and addition tests as suggested by Frank, et al. [To better understand the effect of volatiles on flavour liking of dry/wet aged beef, a PLS regression model was established using flavour liking of aged beef samples as Y and their corresponding concentration of selected volatiles as X . A moder, et al. .Results from this study indicated that the dry aged and wet aged beef exhibited similar volatile profiles measured by GC-MS and GC-O. The concentrations of several odour- and flavour-active volatiles varied with ageing method and ageing time. The difference in flavour liking of aged beef could be explained by their corresponding volatile profiles. The preferred flavour liking of the dry aged beef samples described in a previous study could be attributed to some differences in volatile profiles, especially alkyl-pyrazines, 2-acetyl-2-thiazoline, and 3-hydroxy-2-butanone. However, the increased lipid oxidation and loss of desirable volatiles, e.g., 2-acetyl-1-pyrroline and alkyl-pyrazines, in the 56-day ageing can lead to the decrease in flavour liking. In wet aged beef, the accumulation of bacterial fermentation products appeared to be detrimental to its flavour. Therefore, based on the conditions used in this study, 35-day dry ageing is an optimal aging period to produce beef with a desirable volatile profile and flavour. Prolonged (56-day) ageing should be avoided in both dry ageing and wet ageing to prevent the deterioration of flavour, however further studies should be performed to confirm this.


P=0.188; 0.872 for DWI, P=0.181; and 0.906 for T2WI+DWI, P=0.676). The results showed that 3D V-Net architecture could conduct reliable rectal cancer segmentation on T2WI and DWI images. All-relevant radiomics-based models presented similar performances in KRAS/NRAS/BRAF prediction between the two segmentation manners.Radiomic features extracted from segmented tumor regions have shown great power in gene mutation prediction, while deep learning–based (DL-based) segmentation helps to address the inherent limitations of manual segmentation. We therefore investigated whether deep learning–based segmentation is feasible in predicting KRAS/NRAS/BRAF mutations of rectal cancer using MR-based radiomics. In this study, we proposed DL-based segmentation models with 3D V-net architecture. One hundred and eight patients’ images (T2WI and DWI) were collected for training, and another 94 patients’ images were collected for validation. We evaluated the DL-based segmentation manner and compared it with the manual-based segmentation manner through comparing the gene prediction performance of six radiomics-based models on the test set. The performance of the DL-based segmentation was evaluated by Dice coefficients, which are 0.878 ± 0.214 and 0.955 ± 0.055 for T2WI and DWI, respectively. The performance of the radiomics-based model in gene prediction based on DL-segmented VOI was evaluated by AUCs , which were comparable to that of corresponding manual-based VOI (0.637 for T2WI,  It is clear that  EpidermaUp to now, it is still a state-of-the-art routine practice to detect gene mutation status by pathologically analyzing biopsy samples or resected tissues. However, there is a growing recognition  that tisThere have been a certain number of researches based on PET/CT , CT 7),,7), or Mvia 3D V-Net on T2WI and DWI and then compare the performance of radiomics in predicting the KRAS/NRAS/BRAF status between DL-based auto segmentation and manual-based segmentation.Radiomics, with its high-throughput quantitative image features, has shown exciting power in assessing treatment response , genetic3 on MR image (n=7); (e) other pathological types of tumor  (n=23). Among the 202 participants, 94 patients were subject to a KRAS/NRAS/BRAF mutation test, and the interval was less than 4 weeks between MR imaging and the gene test. Among the 94 patients who underwent the gene test, 53 patients harbored mutant KRAS/NRAS/BRAF, and 41 patients were wild type. The remaining 108 patients were not tested for mutations and could not be used to assess mutation prediction, but they were suitable for modeling segmentation. Therefore, we used the 108 patients without the gene test as the training set for the auto segmentation model and the 94 patients with the gene test as the test dataset, each including both the T2WI and DWI images. The radiomics-based model for gene mutation prediction was constructed based on 94 patients’ MR images via 5-fold cross validation. Considering the different imaging modalities and tumor segmentation manners, we constructed six radiomics-based models, which were T2WI+manual-based VOI, T2WI+DL-based VOI, DWI+manual-based VOI, DWI+DL-based VOI, T2WI+DWI+manual-based VOI, and T2WI+DWI+DL-based VOI. The detailed experiment flow chart is shown in This retrospective study was approved by the institutional review board in our hospital, and informed patient consent was waived. A total of 202 participants  with rectal adenocarcinoma confirmed by colonoscopy biopsy were recruited from 333 patients who had underwent pelvic MR imaging on a 3.0T scanner (November 2016 to May 2019) after screening according to the following exclusive criteria: (a) treated with any strategy before MR imaging or surgery (n=75); (b) the interval between MR imaging and postoperative pathology was more than 4 weeks (n=8); (c) gross artifacts or severe distortion of MR images (n=18); (d) absence of visible lesion or the volume of lesion was less than 1 cm2). An oblique axial T2WI high-resolution sequence was planned perpendicularly to the bowel with the tumor, while the axial DWI sequence was performed parallelly to the horizontal line.All MR scanning was performed on a 3.0T MR scanner  with an eight-channel phased-array coil. Bowel preparation was implemented by drinking folium sennae soup (a kind of laxative) after dinner the night before the examination. Antispasmodic and other intestinal contrast agents were not used. Rectal MRI protocols included axial T1WI (TR/TE = 487/8 ms), coronal and sagittal T2WI , oblique axial small FOV FRFSE T2WI , and axial single-shot EPI DWI  images by one radiologist with 8 years of experience in abdominal MRI and scrutinized by another senior abdominal MRI radiologist with 20 years of experience. The regions of contiguous normal rectal wall and lumen against tumor were manually labeled on T2WI images, and the magnetic susceptibility artifacts were labeled on DWI images, which were used for the training and validation of the automated tumor segmentation algorithm. All manual delineations were performed using ITK-SNAP (version 3.8)  . BecauseAll MR images were normalized to accelerate the convergence of neural network training. First, the MR images were resampled to the same spatial resolution: 0.4×0.4×3.3 (mm), and then the gray values were linearly normalized into the range . Considering the GPU memory, the input 3D patch size was set to 96×96×32. Due to the limited amount of training images, image augmentation was performed, which included shift, rotation, scale, and flip slightly.We applied cascade learning in this work based on 3D V-Net for the tumoral tissue segmentation of the rectum on T2WI and DWI sequences. The code of 3D V-Net was improved from the V-Net . The arcN is the number of voxels of the image, ip is the prediction probability of the i-th voxel which belongs to the target region, and ig denotes whether the i-th voxel belongs to ground truth annotation or not . The volume size of the input and output image is 512×512×30, and the parameters of spacing for the coarse model and fine model are  and , respectively. Similar to other CNN, the training process was iterated with min-batch and stochastic gradient descent to ensure quick convergence. Tumor volume was segmented using forward propagation in the test process.Where The tissue blocks were acquired from resected tumors, and pathologists selected the samples for gene mutational analysis. Genomic DNA was extracted from 5 mm formalin-fixed, paraffin-embedded (FFPE) tumor tissue sections, using a DNA FFPE Tissue Kit . KRAS , NRAS , and BRAF  mutations were detected by using polymerase chain reaction (PCR) and amplification-refractory mutation system (ARMS). Among the 53 patients with mutant genes, 48 patients were KRAS mutation, four patients were NRAS mutation, and one was BRAF mutation.https://pyradiomics.readthedocs.io/en). First, a total of 2,600 features were extracted from the labeled tumor volume of each MR sequence. These features were computed by the combination of 104 original image features with 25 image filters. The original image features include First-order, Shape, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), Gray Level Dependence Matrix (GLDM), and Neighborhood Gray-Tone Difference Matrix (NGTDM). The image filters consist of Gaussian noise, curvature flow, Laplacian of Gaussian, Discrete Gaussian, Speckle noise, Recursive Gaussian, shot noise, and Wavelets. Second, feature selection was performed on the extracted features  by least absolute shrinkage and selection operator (Lasso) method to work out an optimal feature subset . We set two parameters for LASSO, the feature scaler and shrinkage penalty, as min-max scaler and 0.02, respectively. The selected features for each radiomics-based model are presented in the supplementary material. Then, a radiomics-based model was built by support vector machine (SVM) classifier with the selected features. The parameters of SVM consist of penalty factor C (3.0), Gamma (0.03), and kernel . The predict models were verified by five-fold cross-validation and thus derived an average performance.Radiomics analysis was performed by a clinical research platform . The code for radiomics analysis was developed based on pyradiomics , accuracy, sensitivity, and specificity were calculated to evaluate the performance of the radiomics-based model in differentiating gene status. DeLong’s test was used to compare two AUCs of the manual based model and deep learning–based model of identical imaging modality. The statistical analyses were conducted with SPSS (version 26.0), Medcalc (version 20.0), and PyCharm . A two-sided p value < 0.05 was statistically considered significant difference.Differences of patient baseline characteristics between the wild-type and mutant groups were tested using independent samples The ground truth annotation includes 202 rectal cancers on T2WI and DWI sequences. To evaluate the performance of the 3D V-Net, the Dice Similarity Coefficient (DSC) was used to compare segmentations between AI and a radiologist. The volumetric segmentations generated from the deep learning model are probability maps. The mean and standard deviation of the Dice is 0.878 ± 0.214 and 0.955 ± 0.055 for T2WI and DWI separately in the test dataset. A paradigm of tumor segmentation results are shown in p = 0.021). It seems that a tumor with more advanced T stage is prone to evolve mutant gene  was achieved by V-Net in our study compared with Trebeschi’s . When referring to radiomics analysis, the genotype prediction performance of DWI is superior to that of T2WI, and combination modality surpasses any single imaging modality no matter whether it is manual-based VOI or DL-based VOI (P=0.181). We speculate that limited sample size may be one reason. On the other hand, DL-based VOI may contain some peritumoral region, which could exhibit an inflammatory response and tumor microinvasion. The inflammatory response and tumor microinvasion may provide additional information that is related to gene mutation. Meng et al.  is strongly correlated with response to anti-EGFR therapies , we evalased VOI . Tumors ased VOI . The DWIased VOI  developeg et al.  investigg et al.  or PET/Cg et al.  on genotThough 202 patients were involved in our analysis, it is still necessary to validate this framework and compare it with different architectures, such as the recently developed Generally Nuanced Deep Learning Framework , in largIn this study, 3D V-Net architecture provided reliable rectal cancer segmentation on T2WI and DWI compared with expert-based segmentation, and auto segmentation was subjected to radiomics analysis in the prediction of KRAS/NRAS/BRAF mutation status and may produce a good prediction result.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by institutional review board of Xijing hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.GZ and LC produced the manuscript. GZ and LC conceived and designed framework of this article. GZ, LC, AL, XP, JS, and YeH collected and analysed the data. JZ and YiH supervised this study and reviewed the manuscript. All authors contributed to the article and approved the submitted version.This research was funded by Key Research and Development Projects in Shaanxi, grant number 2018ZDXM-SF-059.Authors LC, AL and XP were employed by Shanghai United Imaging Intelligence Co., Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The results are comparable and indicate this method can be used for edible oil flavor analysis.An efficient and effective multiple headspace-solid phase microextraction-arrow-gas chromatography-mass spectrometry (MHS-SPME-arrow-GCMS) analytical protocol is established and used to quantify the flavor compounds in oils. SPME conditions, such as fiber coating, pre-incubation temperature, extraction temperature, and time were studied. The feasibility was compared between SPME-arrow and the traditional fiber by loading different sample amounts. It was found that the SPME-arrow was more suitable for the MHS-SPME. The limit of detection (LODs) and limit of quantitation (LOQs) of pyrazines were in the range of 2–60 ng and 6–180 ng/g oil, respectively. The relative standard deviation (RSD) of both intra- and inter-day were lower than 16%. The mean recoveries for spiked pyrazines in rapeseed oil were in the range of 91.6–109.2%. Furthermore, this newly established method of MHS-SPME-arrow was compared with stable isotopes dilution analysis (SIDA) by using [ Good natural flavor is one of the major drivers for consumers to purchase edible oils, especially in Asia. Flavor-enhanced edible oils are normally produced by high roasting temperature in mechanical pressing. Under high temperatures in the processing, a lot of flavor volatiles are created through various reactions, in particular the Mallard reactions. Pyrazines are a group of such volatiles that attract the most importance. A lot of studies have shown that pyrazines are formed by roasting, baking, or thermally processing through Maillard reactions and impart cocoa, peanut, roasted nut-like flavors to various foods ,2,3.Generally, pyrazines, which exhibit high odor activity values, are highly correlated with roasted flavor in roasted black tea, coffee, and oils ,5,6,7. FThe above pyrazines were also found to be aroma-active compounds in flavor-enhanced rapeseed oils and roasted pumpkin seed oils ,11. InteThe conventional methods of extracting the aroma compounds of oils mainly include headspace solid-phase microextraction (HS-SPME), solvent-assisted flavor evaporation (SAFE), simultaneous distillation extraction (SDE), dynamic headspace (DHS), static headspace (SHS), and stir bar sorptive extraction (SBSE) ,13,14,15SPME-arrow fiber is a new device, which has 6–20 times larger than the sorbent phase of traditional SPME fiber and shows more than 10 times the sensitivities and extraction efficacies , Korean The HS-SPME technique is based on the distribution of target analyte in the headspace, sample matrix, and SPME fiber coating. Compared to other methods, SPME is a non-exhaustive extraction technique, which means only a small portion of target analytes can be extracted every time . Previoun-hexadecane showed that the peak area obtained by MHS-SPME were statistically equal, which proved that MHS-SPME could effectively eliminate the matrix effect -2-methyl-pyrazine.The aim of this study is to set up a novel and more accurate MHS-SPME-arrow-GC-MS method to quantify pyrazines in flavor-enhanced edible oils. This is the first time applying MHS-SPME-arrow to quantify flavor compounds in oils combined with internal standard (ISTD) while carried out in organic solvent for quantitative calibrations. There are three steps needed to develop the quantitative method based on MHS-SPME-arrow: (1) Optimizing the HS-SPME conditions for the maximum extraction efficiency and sensitivity improvement; (2) finding the appropriate sample loading suitable for MHS-SPME and clarifying the feasibility of SPME-arrow fiber in replacement of the traditional SPME fiber; and (3) validating the MHS-SPME-arrow method combined with the internal standard in solvent by selected conditions, in which the MHS-SPME-arrow and SIDA were compared using [2H6]-2-methyl-pyrazine was purchased from CDN Isotopes Inc. . Caprylic capric triglyceride (ODO) was from Maitian Chemical Co. Ltd. .Thirteen pyrazine standards  and internal standard of 3-methyl-pyridine were purchased from Sigma-Aldrich Co. Ltd. . Isotopically labeled internal standard: [Four SPME-arrow fibers, including polydimethylsiloxane (PDMS) (100 µm × 20 mm), polyacrylate (PA) (100 µm × 20 mm), polydimethylsiloxane/divinylbenzene (PDMS/DVB) (120 µm × 20 mm), polydimethylsiloxane/divinyl-benzene/carboxen (PDMS/DVB/CAR) (120 µm × 20 mm), and traditional SPME DVB/CAR/PDMS (50/30 µm × 10 mm) fibers were purchased from CTC Analytics AG . The holder of fibers was purchased from PAL System .The refined pyrazine-free and flavor-enhanced rapeseed oils were obtained from the local supermarket, thus as the real samples of 3 flavor-enhanced oils, including peanut oil, sesame oil, and rapeseed oil. All oil samples were stored at 4 °C. Ethyl acetate was chromatographic grades.Standard solutions were prepared using ethyl acetate as the solvent. The combined stock solution of target analytes (13 pyrazines) and internal standard (3-methyl-pyridine) were prepared by weighing and storing in sealed amber glassware at 0 °C in the dark. The concentration of analytes (pyrazines and 3-methyl-pyridine) in stock solutions were approximately 1000 mg/L, and the standard calibration solution (approximately 25 mg/L) was prepared by diluting the stock solution accordingly.A stock solution of the internal standard 3-methyl-pyridine (2000.0 mg/kg) was prepared by adding 20.0 mg of 3-methyl-pyridine to 10.0 g of ODO. Then, the stock solution of the internal standard was diluted to 50.0 mg/kg with ODO. The resulting solutions were stored at 0 °C in the dark.The resulting internal standard solution (50.0 mg/kg) of 50.0 mg was added to 1.0 g of the oil sample, homogenized by vortex mixer afterward. The oil samples with internal standard (50.0 mg) were weighed into a 20 mL headspace vial sealed with a PTFE/silicone septum screw-cap. The vials were placed in the autosampler tray for HS-SPME analyses. The procedure for stable isotope dilution assay was the same as above.For the HS-SPME method, the oil samples were pre-incubated at 80 °C for 20 min with the agitation speed of 450 rpm to release the volatile compounds prior to extraction, and then the fiber was exposed in the headspace of the vial at 50 °C for 50 min for equilibrium extraction. After the extraction of volatiles from the oil onto the fiber, the analytes were thermally desorbed from the fiber in the injector port of the chromatograph for 80 s and transferred to the chromatograph column where volatile compounds were separated. Finally, the analytes were identified and quantified by a mass spectrometer. After extraction and desorption, the SPME fiber was conditioned at 230 °C for 3 min. In the MHS-SPME method, the oil samples were taken 4 times at equal time intervals (of about 70 min).A Combi PAL ingenious sample handling system  used as an autosampler was mounted into the gas chromatograph. The change of liquid injector tool and SPME-arrow tool were the robotic steps for the analytical process. The gas chromatograph system was an Agilent 8890 coupled with a 5977B mass spectrometer . A DB-FFAP analytical column  from Agilent Technologies was carried out to separate analytes. The SPME fiber device, which was used as an injector, was desorbed at high temperatures in the gas chromatography injector port to transfer analytes to gas chromatography device.GC conditions: Fiber desorption time: 80 s; injector temperature: 230 °C; injection mode: Splitless; carrier gas: Helium (99.999% purity); flow rate: 1.0 mL/min. The oven temperature program was as follows: The initial temperature was 40 °C (1.5 min); the temperature was programmed from 40 to 100 °C (5 min) at 10 °C/min, from 100 to 150 °C (10 min) at 2 °C/min, from 150 to 185 °C at 5 °C/min, from 185 to 245 °C (8 min) at 20 °C/min, for a total running time of 65.5 min.MSD conditions: MS was operated in EI mode (70 eV); acquisition was carried out in full scan and selected ion monitoring (SCAN&SIM) mode; and the selected ions were reported in T) can be calculated by Equation (1):i is the peak area of analytes in the ith extraction, A1 is the peak area of analytes in the first extraction, β is a constant between zero and one (0 ≤ β < 1), which can be calculated from Equation (2):MHS-SPME is a process based on stepwise extraction of the analytes from the same sample, which can be seen as a combination of multiple headspace and headspace solid-phase microextraction ,32. Aftei and i − 1, where ln β is the slope of the linear plot and can be calculated from a limited number (3 or 4) of extractions. Thus, the total amount of analytes in the system can be quantified by ISTD.There is a linear relationship between ln AThe detector response factors were calculated by the ethyl acetate solution of pyrazines and the internal standard. A liquid injection mode was used in this process by GC-MS. The response factors were calculated before samples were analyzed by MHS-SPME.Results were analyzed using ANOVA carried out using SPSS Statistical Software 18.0 , and the confidence interval was taken as 95%. All figures were generated using Origin 9.0, Adobe Illustrator, and Adobe Photoshop.The experiments were performed on the flavor-enhanced rapeseed oil. Variables such as type of SPME-arrow fiber, pre-incubation temperature, extraction temperature, and time were studied to optimize the performance of HS-SPME. The HS-SPME conditions were optimized on the basis of peak areas of analytes and performed three times repeatedly. p < 0.05) (p < 0.05). The single-phase of PDMS (non-polar) and PA (polar) showed the poorest performance. These results were consistent with previous studies [The sensitivity and selectivity of the extraction method were determined by the properties of adsorbents on SPME fibers. The selection of a suitable fiber was the first step in developing the HS-SPME method. The performance of SPME fibers for the target compounds depends on the polarity of the analytes and the physical and chemical properties of coating types . In this < 0.05) A. PDMS/D studies . TherefoAfter the type of coating was selected, the second important parameter was pre-incubation temperature. The role of the pre-incubation procedure is to volatilize the aroma compounds from the sample and build equilibrium between the sample and headspace. At the same pre-incubation time, the increase of temperature can promote the distribution of weak volatile components in the headspace and accelerate the building of equilibrium. However, higher temperatures may lead to the conversion and degradation of unstable substances. Five pre-incubation temperatures were studied: 20, 40, 60, 80, and 100 °C. The results B showed Extraction temperature is an important factor affecting the extraction efficiency of aroma compounds. The increasing temperature is conducive to the release of aroma compounds from the matrix at the same extraction time. However, high temperatures may also cause the degradation of components in the food matrix and decrease the absorbent ability of SPME fibers. Five extraction temperatures were studied: 40, 50, 60, 70, and 80 °C. According to The effect of different extraction times  was also evaluated at 50 °C. The optimum time was required to reach equilibrium in three phases: The fiber coating, the headspace, and the sample. SPME under equilibrium is an important condition for carrying out the MHS-SPME operation. Compared with extraction without equilibrium, extraction under equilibrium has better repeatability . As showOn that basis of the above experiments, the PDMS/DVB/CAR (120 µm × 20 mm) fiber coating was chosen, and the optimal HS-SPME conditions were pre-incubation temperature: 80 °C; extraction temperature: 50 °C; and extraction time: 50 min.i versus the extraction numbers (i − 1), where ln Ai shows linear decay with the number of extractions (Equation (2)). In order to satisfy that requirement, 0.4 < β < 0.95 should be fulfilled, which indicates the slope of the linear plot ln Ai versus i−1 must less than −0.0513 and more than −0.9163 [β and coefficient of determination (R2) were studied on flavor-enhanced rapeseed oils using the 20 mL HS vial.The basic operation of MHS-SPME involves repeating extraction several times from the same sample, while the peak area decreases exponentially with the number of extractions. Therefore, the amounts of the sample should not only meet the LOQs but also ensure the linearity between ln A −0.9163 . Compare −0.9163 . In this2 > 0.75, (2) 0.4 < β < 0.95, indicating that the ln Ai was linearly decayed with the number of extractions. 2 > 0.75 while β < 0.95. As shown in β satisfied the MHS-SPME applicable requirement, regardless if the traditional SPME fiber or SPME–arrow fiber was used. The reason might be that the headspace was saturated in the multiple headspace extractions. However, the low sample amount may lead to poor repeatability due to the mass of analytes closed to LOQs, which means that the SPME–arrow fiber was more suitable for the quantitation of pyrazines in flavor oils by MHS-SPME. Therefore, 50.0 mg was selected as the sample mass in the following study.For MHS-SPME, two conditions should be met: (1) RThe analytical performance of the MHS-SPME-arrow-GC-MS was evaluated in terms of LOD, LOQ, inter-day precision, intra-day precision, and recovery, as shown in This method allows the analysis of a variety of different pyrazines, and the selected internal standard is pyridine with a similar structure to pyrazines. Therefore, good selectivity is a necessary requirement for the correct identification and quantification of all analytes. The identification and quantitation of target compounds, including internal standard, were based on the retention time and different selected ions, which are listed in For the LOD and LOQ determinations, the refined rapeseed oil (“pyrazine-free”) was used as the matrix to prepare standard solutions. The suitable concentration was established as LOD and LOQ of pyrazines with the signal-to-noise ratio (S/N) of pyrazines being 3.0 and 10.0, respectively . As showTo evaluate the precision of the method, five parallel experiments were carried out on commercial flavor-enhanced rapeseed oils to calculate the intra-day precision. The inter-day precision was determined by analyzing the same sample five times a week for three weeks. Both intra-day precision and inter-day precision were expressed as the relative standard deviation (RSD). The results showed that the RSD of intra-day and inter-day were both lower than 16%, verifying the good precision of the MHS-SPME-arrow method. Model experiments were also carried out to evaluate accuracy.Three spiked concentration levels in refined rapeseed oil were also studied by the standard addition method (SA). The results in 2H6 as the stable isotope internal standard. The concentration of 2-methyl-pyrazine was 1.21 ± 0.13 µg/g for SIDA and 1.43 ± 0.11 µg/g for MHS-SPME-arrow, respectively. There was no difference (p >0.05) by statistical analysis. Thus, the data showed that the MHS-SPME-arrow could be applied to the analysis of these pyrazines in oil samples. By this conclusion, the method has been set up after condition optimization and SIDA method verification. An efficient and effective MHS-SPME-arrow-GCMS analytical protocol is established and can be used to quantify the flavor compounds in oils.The stable isotope dilution analysis (SIDA) has proven to be very precise in the model experiments, even at a very low extraction yield . The mai2 and slope of the linear plot ln Ai versus i − 1, were shown in Flavor-enhanced oils are usually produced by the traditional pressing process, mainly including roasting, pressing, and filtering. Heterocyclic volatiles are commonly produced by the Maillard reactions among proteins, amino acids, and sugars during oilseed roasting . Pyrazin2 > 0.75 and −0.9163 < slope < −0.0513. The results showed that the R2 of pyrazines in three samples was greater than 0.98, of which more than 90% was bigger than 0.99. All the slopes were in the range of −0.9163~−0.0513. It was shown that the ln Ai decreased linearly with the number of extractions, and the linear relationship was good, fitting to the MHS-SPME-arrow method.Thirteen pyrazines were found in three oils. According to the discussion in p < 0.05). The total concentrations of pyrazines in the three samples were, in order from high to low: Sesame oil, peanut oil, and rapeseed oil. The total amount of pyrazines in sesame oil was close to 14 times that of rapeseed oil. The pyrazines with the highest concentration were 2-methyl-pyrazine and 2,5-dimethyl-pyrazine in three oils. As described by previous studies [There were significant differences in the concentrations of pyrazines in different types of oils ( studies , pyrazinIn summary, a reliable MHS-SPME-arrow-GC-MS method combined with ISTD for the quantitation of 13 pyrazines in flavor oils was developed. The SPME-arrow fiber was found to be more suitable for the MHS-SPME method, and the PDMS/DVB/CAR fiber was selected. The highest efficiency was performed under selected HS-SPME conditions. The novel method was verified with the stable isotope dilution analysis method and showed high sensitivity and accuracy. Additionally, the pyrazines of three market product samples were analyzed and quantified using the new method, which proved that the MHS-SPME-arrow-GC-MS was suitable to quantify pyrazines in oils. In the future development, this method has great opportunities in the quantitation of other aroma active compounds such as alcohols and pyrroles. At the same time, as an absolute quantitative method to eliminate the matrix effect, this method also has an obvious limitation. That is that it requires multiple extractions for a single analysis, which means it takes a much longer time than the conventional HS-SPME-GC-MS. Therefore, there are some aspects for further improvement for this newly proposed method.


Chronic inflammatory pain is a common clinical disease that tends to be associated with negative emotions such as anxiety and depression. The anterior cingulate cortex (ACC) is involved in pain and pain-related anxiety, and γ-aminobutyric acid (GABA)-ergic interneurons play an important role in chronic pain and anxiety. Electroacupuncture (EA) has good analgesic and antianxiety effect, but the underlying mechanisms have not yet been fully elucidated. In this study, we established a chronic inflammatory pain model and observed that this model induced anxiety-like behaviors and decreased the numbers of parvalbumin (PV) and somatostatin (SOM) positive cells. Activation of PV but not SOM interneurons by chemogenetic techniques alleviated anxiety-like behaviors and pain sensation. EA treatment improved pain sensation, anxiety-like behaviors and increased the number of PV- positive cells in the ACC, but did not affect on the number of SOM-positive cells in the ACC. Moreover, specific inhibition of PV interneurons by chemogenetic methods reversed the analgesic and antianxiety effects of EA. These results suggest that EA ameliorates chronic inflammatory pain and pain-related anxiety by upregulating PV but not SOM interneurons in the ACC. Chronic pain not only causes painful physical sensations but also often accompanies adverse emotional reactions such as depression and anxiety . It has Electroacupuncture (EA) is a treatment method that combines traditional acupuncture with electrical nerve stimulation based on the basic theory of traditional Chinese medicine. Both animal and human studies have shown that EA can significantly improve chronic inflammatory pain and pain-induced disorders . Our preIt is well known that dysfunction of central γ-aminobutyric acid (GABA)-ergic interneurons is associated with anxiety and depression . RecentlIn this study, we aimed to investigate whether PV or SOM interneurons in the ACC contribute to anxiety-like behavior induced by chronic inflammatory pain and whether EA modulates chronic inflammatory pain-related anxiety-like behaviors through these interneurons. To test this hypothesis, we established a chronic inflammatory pain rat model with Complete Freund’s Adjuvant (CFA) and employed behavioral tests (pain and anxiety-like behaviors) and immunohistochemical and chemogenetic techniques to determine the underlying mechanisms of EA in the treatment of chronic inflammatory pain-related anxiety.ad libitum. All experiments were conducted at 9:00–17:00 each day.Adult healthy male Sprague-Dawley (SD) rats  were group housed with a maximum of 4 animals in individual cages under controlled conditions  and given food and water A chronic inflammatory pain model was established by subcutaneously injecting 0.1 mL CFA  into the left hind paw of the rats. Rats in the control group were injected with the same volume of sterile 0.9% saline.12 vg/mL) and rAAV-Ef1α-DIO-hM3D(Gq)-mCherry-WPRE (titer: 5.72 × 1012 vg/mL) were premixed at a ratio of 1:1 for the specific activation of PV interneurons; rAAV-fPV-CRE-bGH PA (titer: 2.64 × 1012 vg/mL) and rAAV-Ef1α-DIO-hM4D(Gi)-mCherry-WPREs (titer: 5.63 × 1012 vg/mL) were premixed at a 1:1 ratio for the specific inhibition of PV interneurons; rAAV-fSST-CRE-bGH PA (titer: 2.19 × 1012 vg/mL) and rAAV-Ef1α-DIO-hM3D(Gq)-mCherry-WPRE (titer: 5.72 × 1012 vg/mL) were premixed at a ratio of 1:1 for the specific activation of SOM interneurons; and rAAV-PV-mCherry-pA (titer: 5.10 × 1012 vg/mL) and rAAV-SST-mCherry-pA (titer: 2.70 × 1012 vg/mL) were control viruses. A 400 nL volume of virus was injected into the ACC : +1.4 mm) at a rate of 50 nL/min. The activating virus was injected on the right side, and the inhibitory viruses were injected on both sides. The model was established at 7 days after virus injection. Five weeks after the virus injection, behavioral tests were measured 30 min after administration of Clozapine N-oxide . All viruses used in this study were provided by Wuhan BrainTVA Co., Ltd.All rats were acclimatized to the laboratory environment at least 7 days before surgery. A stereotactic frame  was used for craniotomy under deep anesthesia with 2% isoflurane. Skulls were fully exposed to locate bregma and lambda and drilled using a dental drill  at the target location. A 10-μL WPI nanofill syringe was connected to a microsyringe pump controller . For chemogenetic manipulations, rAAV-fPV-CRE-bGH PA . Anxiety-like behaviors in rats were determined using the open field (OF), elevated zero maze (EZM), and novelty-suppressed feeding (NSF) tests. Moreover, we only performed one behavioral test 1 day by the same operator during 9:00–17:00.∧ (xf + k × δ-4). “xf” was the logarithmic value of the von Frey hair strength last used, “k” was the corresponding value of the resulting sequence in the k-value table, and “δ” was the mean difference between stimuli . If the result was greater than 26 g or less than 0.4 g, it was recorded as 26 or 0.4 g.Paw withdrawal thresholds was measured with a series of von Frey hairs  using the up-down method . Rats weThe OF test is one of the most commonly used tests of anxiety behavior because it triggers a conflict between an animal’s desire to explore and its fear of venturing into the OF . After 3The EZM test is an improved version of the elevated plus maze and involves the conflict between an animal wanting to explore new areas, and not wanting to leave the protected closed arm . The cirThe NSF test is often used to assess anxiety-like behavior because it triggers a conflict between an animal’s urge to feed and its fear of venturing into the center of the box in a bright new environment . We prepThe EA intervention was conducted from day 26 to 31. The rats in the EA group were fixed and locally disinfected. Acupuncture needles (specification 0.25 mm × 13 mm) were inserted at bilateral Zusanli (ST36) and Kunlun (BL60) acupoints to a depth of 5 mm, and then the HANS–200A Acupoint Nerve Stimulator  connection was made at the bilateral ST36 and BL60. The EA stimulation parameters were as follows: 2/100 Hz, 0.5–1.5 mA . Shallow punctures into the subcutaneous acupoints without current delivery were administered to rats in the sham EA group. All rats were prepared with the same fixation method.In the chemogenetic experiments, CNO was intraperitoneally injected first, followed by the EA intervention for 30 min.® 488)  for 1 h at 37°C. The sections were washed with TBST six times. Then, the sections were incubated with diamidino-phenyl-indole (DAPI). When dry, the sections were cover-slipped. Images were scanned using the Imager M2 microscope . Quantitative analyses of the number of positive cells in the ACC were performed using ImageJ software.Rats under deep anesthesia with pentobarbital  were transcardially perfused with 4°C saline followed by 4% paraformaldehyde in 0.1 M PBS  after the last behavioral test. It is worth noting that the rats had to be killed within 2 h after CNO injection. Brains were harvested and post-fixed for 24 h in 4% paraformaldehyde at 4°C, followed by sequential dehydration in 15 and 30% sucrose. OCT-embedded brains were sectioned into 30-μm-thick sections on a cryostat microtome NX50 . Sections were rinsed in TBST four times and incubated for 1 h at 37°C with 10% donkey serum (0.3% Triton X-100/TBST buffer). The sections were incubated overnight at 4°C with the following antibodies: rabbit anti-PV , rabbit anti-SOM , rabbit anti-VIP , and rabbit anti-NPY . Subsequently, the sections were washed six times in TBST and incubated with pre-adsorbed secondary donkey anti-rabbit IgG H&L  and were analyzed by SPSS 20.0. Repeated measures analysis of variance (ANOVA) was used to analyze the PWTs followed by Bonferroni’s P > 0.05; P < 0.01; The flow chart of the experimental design is shown in P < 0.01; P < 0.01; P < 0.05; P > 0.05; Twenty-eight days after CFA injection, compared with control group, the model group rats displayed multiple anxiety-like behaviors, including behavior in the OF test -CNO group were significantly higher than those in the model-PV-mCherry-CNO group after CNO administration -CNO group exhibited an increase in the time and percentage of distance in central area relative to the model-PV-mCherry-CNO group in the OF test (P > 0.05; We then specifically activated SOM interneurons to observe their effects on pain-related anxiety-like behaviors based on the fluorescence results. Before this, we first examined the specificity of the virus, and the immunofluorescence results showed that cells with mCherry signals could be observed in the ACC and had high co-localization with SOM-positive cells . PWTs ofP > 0.05; P < 0.01; P < 0.01; P < 0.01; P < 0.01; P < 0.01; The flow chart of the experimental design is shown in P < 0.05; P < 0.01; P > 0.05; Since EA treatment and activation of PV interneurons in the ACC were shown to have similar effects on alleviating chronic inflammatory pain and pain-related anxiety-like behaviors, the question was whether EA was associated with PV interneurons? To address this question, we detected the number of PV-positive cells in the bilateral ACC after the EA intervention. The number of PV-positive cells in the bilateral ACC were significantly lower in the model group than those in the control group (P > 0.05; In addition, we also detected the number of SOM-positive cells in bilateral ACC after EA intervention. We found that EA treatment had no effect on the number of SOM-positive cells in the bilateral ACC (P < 0.01; P < 0.05; Furthermore, we specifically inhibited PV interneurons in the ACC to assess alterations in the analgesic and antianxiety effects of EA. The location of the virus injection was showed in P < 0.05; P < 0.05; Moreover, after intraperitoneal injection of CNO in rats, time in central area and percentage of distance in central area in the OF test, the time in open arm and the percentage of distance in open arm in EZM test were significantly reduced (In this study, we demonstrated that activation of PV but not SOM interneurons in the ACC alleviated pain sensation and pain-induced anxiety in rats with chronic inflammatory pain. In addition, EA had analgesic and antianxiety effects and increased the expression of PV-positive cells but not SOM-positive cells. Furthermore, inhibition of PV interneurons in the ACC reversed the analgesic and antianxiety effects of EA. Therefore, we determined that EA ameliorated chronic inflammatory pain-related anxiety by upregulating the function of PV interneurons .This series of discoveries provides compelling evidence regarding the high comorbidity between chronic inflammatory pain and anxiety, and this high comorbidity greatly affects people’s physical and mental health and quality of life . Many reActivity of ACC neurons is necessary and sufficient for the coding and processing of pain and the accompanying emotional pain, and the balance between neural excitation and inhibition is necessary for the normal function of the ACC . In chroElectroacupuncture has been recognized in many countries as a kind of complementary and alternative medicine that can treat a variety of diseases . Many acElectroacupuncture ameliorates chronic inflammatory pain and pain-related anxiety by upregulating PV but not SOM interneurons in the ACC.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The animal study was reviewed and approved by the Animal Protection Agency used committee and the Animal Ethics Committee of Zhejiang University of Chinese Medicine.FS and JFF performed the data analysis and wrote the manuscript. MQ, SW, and DX performed the experiments. XS and XH revised the manuscript. JD and JQF designed the experiment. All authors contributed to the manuscript and approved the publication of the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.